Land Classification, Land-Use Areas, and Farm Management Research

1941 ◽  
Vol 23 (3) ◽  
pp. 657
Author(s):  
C. A. Boonstra ◽  
J. R. Campbell
1961 ◽  
Vol 37 (1) ◽  
pp. 42 ◽  
Author(s):  
Howard E. Conklin ◽  
Kenneth C. Nobe

1942 ◽  
Vol 24 (2) ◽  
pp. 392
Author(s):  
J. A. Hodges

2016 ◽  
Vol 11 (1) ◽  
pp. 1 ◽  
Author(s):  
Rosalia Filippini ◽  
Elisa Marraccini ◽  
Sylvie Lardon ◽  
Enrico Bonari

Short food supply chains (SFSCs) have been identified as an economic opportunity for agriculture under urban pressure, as well as drivers for more sustainable farming systems. However, few studies have focused on the intensity of periurban farms that participate in such SFSCs, compared with the performance of the other farms. In this paper, we examined the relationship between agricultural intensity and the market orientation in a representative sample of farms in the urban area of Pisa (Italy). We define <em>agricultural intensity</em> as the intensity of land use and its main drivers (<em>e.g</em>., farm management or the individual characteristics of farmers), and <em>market orientation</em> as the ratio of farm produce within conventional, short or mixed foodsupply chains. The results suggest that the market orientation of periurban farming systems is more correlated to the indicators of farm management and land use intensity than to the individual farmer’s characteristics. This result provides the first evidence that market orientation is a driver of intensity, and that individual farmer’s characteristics are not significantly different in the three groups of market orientation. These findings could be generalised to other urban areas and correlated with the main orientation of farming systems in order to support both the assessment of farming systems and the implementation of innovative urban food policies.


Author(s):  
Trinh Le Hung

The classification of urban land cover/land use is a difficult task due to the complexity in the structure of the urban surface. This paper presents the method of combining of Sentinel 2 MSI and Landsat 8 multi-resolution satellite image data for urban bare land classification based on NDBaI index. Two images of Sentinel 2 and Landsat 8 acquired closely together, were used to calculate the NDBaI index, in which sortware infrared band (band 11) of Sentinel 2 MSI image and thermal infrared band (band 10) of Landsat 8 image were used to improve the spatial resolution of NDBaI index. The results obtained from two experimental areas showed that, the total accuracy of classifying bare land from the NDBaI index which calculated by the proposed method increased by about 6% compared to the method using the NDBaI index, which is calculated using only Landsat 8 data. The results obtained in this study contribute to improving the efficiency of using free remote sensing data in urban land cover/land use classification.


1958 ◽  
Vol 40 (2) ◽  
pp. 434
Author(s):  
Emery N. Castle

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